IDEAS home Printed from https://ideas.repec.org/a/bjf/journl/v10y2025i7p08-16.html
   My bibliography  Save this article

Implementation of an Adaptive Cyber Deception Attack Management Using Deep Learning Framework

Author

Listed:
  • Odo Francisca E.

    (Computer Science Department, Enugu State University of Science and Technology)

  • Asogwa T.C.

    (Computer Science Department, Enugu State University of Science and Technology)

Abstract

This study presents an adaptive threat detection system that leverages Wide Area Neural Networks (WANN) enhanced with a novel trophallaxis-based regularization approach, developed through a Design Thinking-Agile hybrid methodology. The proposed 4-layer WANN architecture, utilizing Rectified Linear Unit (ReLU) activation and trained with Stochastic Gradient Descent (SGD) momentum backpropagation and batch normalization, demonstrated optimal performance with 89% training accuracy and 59% validation accuracy. The performance of the model demonstrates the model’s effectively balancing capacity in complexity and generalizability. When validated against real-world datasets from Ethnos Cyber Limited and ACE-SPED, the integrated system achieved 97.8% attack detection accuracy with

Suggested Citation

  • Odo Francisca E. & Asogwa T.C., 2025. "Implementation of an Adaptive Cyber Deception Attack Management Using Deep Learning Framework," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 10(7), pages 08-16, July.
    • Handle: RePEc:bjf:journl:v:10:y:2025:i:7:p:08-16
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrias/digital-library/volume-10-issue-7/08-16.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijrias/articles/implementation-of-an-adaptive-cyber-deception-attack-management-using-deep-learning-framework/
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjf:journl:v:10:y:2025:i:7:p:08-16. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrias/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.